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CORR
2010
Springer
143views Education» more  CORR 2010»
14 years 7 months ago
CUR from a Sparse Optimization Viewpoint
The CUR decomposition provides an approximation of a matrix X that has low reconstruction error and that is sparse in the sense that the resulting approximation lies in the span o...
Jacob Bien, Ya Xu, Michael W. Mahoney
BMCBI
2011
14 years 5 months ago
Motif-guided sparse decomposition of gene expression data for regulatory module identification
Background: Genes work coordinately as gene modules or gene networks. Various computational approaches have been proposed to find gene modules based on gene expression data; for e...
Ting Gong, Jianhua Xuan, Li Chen, Rebecca B. Riggi...
CORR
2011
Springer
152views Education» more  CORR 2011»
14 years 5 months ago
Sparsity Equivalence of Anisotropic Decompositions
Anisotropic decompositions using representation systems such as curvelets, contourlet, or shearlets have recently attracted significantly increased attention due to the fact that...
Gitta Kutyniok
JC
2010
55views more  JC 2010»
14 years 8 months ago
Dimension-wise integration of high-dimensional functions with applications to finance
We present a new general class of methods for the computation of high-dimensional integrals. The quadrature schemes result by truncation and discretization of the anchored-ANOVA d...
Michael Griebel, Markus Holtz
ICASSP
2011
IEEE
14 years 2 months ago
Co-clustering as multilinear decomposition with sparse latent factors
The K-means clustering problem seeks to partition the columns of a data matrix in subsets, such that columns in the same subset are ‘close’ to each other. The co-clustering pr...
Evangelos E. Papalexakis, Nicholas D. Sidiropoulos